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- W1898976075 abstract "In this paper we present a pixel-level object categorization method suitable to be applied under real-time constraints. Since pixels are categorized using a bag of features scheme, the major bottleneck of such an approach would be the feature pooling in local histograms of visual words. Therefore, we propose to bypass this time-consuming step and directly obtain the score from a linear Support Vector Machine classifier. This is achieved by creating an integral image of the components of the SVM which can readily obtain the classification score for any image sub-window with only 10 additions and 2 products, regardless of its size. Besides, we evaluated the performance of two efficient feature quantization methods: the Hierarchical K-Means and the Extremely Randomized Forest. All experiments have been done in the Graz02 database, showing comparable, or even better results to related work with a lower computational cost." @default.
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- W1898976075 date "2009-01-01" @default.
- W1898976075 modified "2023-10-18" @default.
- W1898976075 title "Efficient Object Pixel-Level Categorization Using Bag of Features" @default.
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- W1898976075 doi "https://doi.org/10.1007/978-3-642-10331-5_5" @default.
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